Rama et al. in [1]} introduced SAA-Net - Scale-Aware Attention Network, proposing a novel multi-branch scale-aware attention network that exploits the hierarchical structure of convolutional neural networks. This hierarchical structure progressively expands the receptive field of the network feature maps, implicitly capturing information at different scales. Inspired by the skip branches in FCN [2]} and SSD [3]}, they proposed to generate multiple density maps from these intermediate feature maps directly. As the feature map generated by the last convolutional layer has the largest receptive field, it carries high-level semantic information that can be used to localize large heads; on the other hand, feature maps generated by the intermediate layers are more accurate and robust in counting extremely small heads (i.e., the crowds), and they contain important details about the spatial layout of the people and low-level texture patterns. To aggregate these maps into the final prediction, they proposed a novel soft attention mechanism that learns a set of gating masks, one for each map. These masks learn to attend to large heads from the density map predicted by the last convolutional layer and smaller ones from earlier layers. Finally, they proposed a new scale-aware loss function to regularize the multi-scale estimates further and guide them to specialize in specific head sizes.
